Brief Description: This seminar is intended as a follow-up to the
version of Q550 Prof. Kruschke taught in Spring 2003. Other students
with previous experience modeling data are also welcome. In this
course we will explore the mechanics of fitting models to data, and
we will delve into theoretical issues in deciding which models fit
best. We will consider topics such as different measures of
discrepancy between data and model predictions, different algorithms
for finding best-fitting parameter values, estimating confidence
intervals for parameter values, detecting and dealing with parameter
redundancy, measures of model complexity, and criteria for model
selection. The material will be discussed mostly at a general,
methodological level, with only occasional application to specific
models in cognitive science. Lots of "hands on" experience will be
gained via work with MATLAB.
Registration Info: Course number P747, section 3885. Requires
graduate standing in a field related to cognitive science or
permission of the instructor.
Time and Place: Spring semester, 2004. Mondays and Wednesdays, 10:10-
11:25am, Psychology Building Room 113.
Required Textbook: Morgan, B. J. T. (2000). Applied Stochastic
Modelling. New York: Oxford University Press.
Other Readings: Recent articles available online to IU students.
Among these will be articles by In-Jae Myung regarding model
complexity. Details TBA.
Software: We will make extensive use of MATLAB. MATLAB is available
on all IU public cluster computers. Details of campus availability
can be found from the Stat-Math Center. MATLAB is available for
purchase in student versions, see its manufacturer, MathWorks.